Abstract
Recent studies propose that some dynamical systems, such as climate, ecological and financial systems, among others, present critical transition points named to as tipping points (TP). Climate TPs can severely affect millions of lives on Earth so that an active scientific community is working on finding early warning signals. This paper deals with the segmentation of a paleoclimate time series to find segments sharing common patterns with the purpose of finding one or more kinds of segments corresponding to TPs. Due to the limitations of classical statistical methods, we propose the use of a genetic algorithm to automatically segment the series together with a method to perform time series segmentation comparisons. Without a priori information, the method clusters together most of the TPs and avoids false positives, which is a promising result given the challenging nature of the problem.
This work has been subsidized by the Ariadna project 13-9202 of the European Space Agency. The research work of M. Pérez-Ortiz, P.A. Gutiérrez, J. Sánchez-Monedero and C. Hervás-Martínez is partially funded by the TIN2011-22794 project of the Spanish Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7508 project of the “Junta de Andalucía” (Spain).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wassmann, P., Lenton, T.: Arctic tipping points in an earth system perspective. AMBIO 41(1), 1–9 (2012)
Allen, M.: Planetary boundaries: Tangible targets are critical. Nature Reports Climate Change, 114–115 (2009)
Lenton, T.M.: Early warning of climate tipping points. Nature Climate Change 1(4), 201–209 (2011)
Dakos, V., Carpenter, S.R., Brock, W.A., Ellison, A.M., Guttal, V., Ives, A.R., Kefi, S., Livina, V., Seekell, D.A., Van Nes, E.H., et al.: Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS One 7(7), e41010 (2012)
Keogh, E., Chu, S., Hart, D., Pazzani, M.: An online algorithm for segmenting time series. In: Proceedings IEEE International Conference on Data Mining, ICDM 2001, pp. 289–296 (2001)
Andersen, K.K., Azuma, N., Barnola, J.M., Bigler, M., Biscaye, P., Caillon, N., Chappellaz, J., Clausen, H.B., Dahl-Jensen, D., Fischer, H., et al.: High-resolution record of northern hemisphere climate extending into the last interglacial period. Nature 431(7005), 147–151 (2004)
Tseng, V.S., Chen, C.H., Huang, P.C., Hong, T.P.: Cluster-based genetic segmentation of time series with dwt. Pattern Recognition Letters 30(13), 1190–1197 (2009)
Sclove, S.L.: Time-series segmentation: A model and a method. Information Sciences 29(1), 7–25 (1983)
Himberg, J., Korpiaho, K., Mannila, H., Tikanmaki, J., Toivonen, H.T.: Time series segmentation for context recognition in mobile devices. In: Proceedings IEEE International Conference on Data Mining, ICDM 2001, pp. 203–210 (2001)
Chung, F.L., Fu, T.C., Ng, V., Luk, R.W.: An evolutionary approach to pattern-based time series segmentation. IEEE Transactions on Evolutionary Computation 8(5), 471–489 (2004)
Xu, R., Wunsch, D.: Clustering. IEEE Press Series on Computational Intelligence. Wiley (2008)
Rand, W.M.: Objective Criteria for the Evaluation of Clustering Methods. Journal of the American Statistical Association 66(336), 846–850 (1971)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2(1), 193–218 (1985)
Peterson, L.C., Haug, G.H., Hughen, K.A., Röhl, U.: Rapid changes in the hydrologic cycle of the tropical atlantic during the last glacial. Science 290(5498), 1947–1951 (2000)
Svensson, A., Andersen, K.K., Bigler, M., Clausen, H.B., Dahl-Jensen, D., Davies, S.M., Johnsen, S.J., Muscheler, R., Parrenin, F., Rasmussen, S.O., Röthlisberger, R., Seierstad, I., Steffensen, J.P., Vinther, B.M.: A 60 000 year greenland stratigraphic ice core chronology. Climate of the Past 4(1), 47–57 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Pérez-Ortiz, M. et al. (2014). Time Series Segmentation of Paleoclimate Tipping Points by an Evolutionary Algorithm. In: Polycarpou, M., de Carvalho, A.C.P.L.F., Pan, JS., Woźniak, M., Quintian, H., Corchado, E. (eds) Hybrid Artificial Intelligence Systems. HAIS 2014. Lecture Notes in Computer Science(), vol 8480. Springer, Cham. https://doi.org/10.1007/978-3-319-07617-1_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-07617-1_29
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07616-4
Online ISBN: 978-3-319-07617-1
eBook Packages: Computer ScienceComputer Science (R0)